Improve speculative execution
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Key: MAPREDUCE-2039
URL: https://issues.apache.org/jira/browse/MAPREDUCE-2039
Project: Hadoop Map/Reduce
Issue Type: Improvement
Reporter: Dick King
Assignee: Dick King
In speculation, the framework issues a second task attempt on a task where one attempt is
already running. This is useful if the running attempt is bogged down for reasons outside
of the task's code, so a second attempt finishes ahead of the existing attempt, even though
the first attempt has a head start.
Early versions of speculation had the weakness that an attempt that starts out well but breaks
down near the end would never get speculated. That got fixed in HADOOP:2141 , but in the
fix the speculation wouldn't engage until the performance of the old attempt, _even counting
the early portion where it progressed normally_ , was significantly worse than average.
I want to fix that by overweighting the more recent progress increments. In particular, I
would like to use exponential smoothing with a lambda of approximately 1/minute [which is
the time scale of speculative execution] to measure progress per unit time. This affects
the speculation code in two places:
* It affects the set of task attempts we consider to be underperforming
* It affects our estimates of when we expect tasks to finish. This could be hugely important;
speculation's main benefit is that it gets a single outlier task finished earlier than otherwise
possible, and we need to know which task is the outlier as accurately as possible.
I would like a rich suite of configuration variables, minimally including lambda and possibly
weighting factors. We might have two exponentially smoothed tracking variables of the progress
rate, to diagnose attempts that are bogged down and getting worse vrs. bogging down but improving.
Perhaps we should be especially eager to speculate a second attempt. If a task is deterministically
failing after bogging down [think "rare infinite loop bug"] we would rather take a couple
of our attempts in parallel to discover the problem sooner.
As part of this patch we would like to add benchmarks that simulate rare tasks that behave
poorly, so we can discover whether this change in the code is a good idea and what the proper
configuration is. Early versions of this will be driven by our assumptions. Later versions
will be driven by the fruits of MAPREDUCE:2037
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